Balchen / Gilles / Waller Dynamics and Control of Chemical Reactors, Distillation Columns and Batch Processes (DYCORD+ '92)

Selected Papers from the 3rd IFAC Symposium, Maryland, USA, 26-29 April 1992
1. Auflage 2014
ISBN: 978-1-4832-9877-1
Verlag: Elsevier Science & Techn.
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 380 Seiten, Web PDF

Reihe: IFAC Symposia Series

In addition to the three main themes: chemical reactors, distillation columns, and batch processes this volume also addresses some of the new trends in dynamics and control methodology such as model based predictive control, new methods for identification of dynamic models, nonlinear control theory and the application of neural networks to identification and control. Provides a useful reference source of the major advances in the field.

Autoren/Hrsg.

Balchen, J. G.

Gilles, E. D.

Waller, K. V.

Rawlings, J. B.

Inhaltsverzeichnis

1;Front Cover
;1
2;Dynamics and Control of Chemical Reactors, Distillation Columns and Batch Processes: (Dycord+' 92);4
3;Copyright Page;5
4;Table of Contents;10
5;3rd IFAC SYMPOSIUM ON DYNAMICS AND CONTROL OF CHEMICAL REACTORS, DISTILLATION COLUMNS AND BATCH PROCESSES (DYCORD+ '92);6
6;INTRODUCTION;8
7;PART I: SURVEY PAPERS;14
7.1;CHAPTER 1.STABILITY CRITERIA FOR CHEMICAL REACTORS;14
7.1.1;Abstract;14
7.1.2;Keywords;14
7.1.3;INTRODUCTION;14
7.1.4;STABILITY CRITERION FOR A BATCH REACTOR;14
7.1.5;STABILITY CRITERION FOR A CSTR;16
7.1.6;STABILITY CRITERION FOR A TUBULAR REACTOR;17
7.1.7;STABILITY CRITERION FOR A FLUIDIZED BED REACTOR;18
7.1.8;STABILITY CRITERION FOR A TUBULAR CATALYTIC REACTOR;19
7.1.9;STABILITY CRITERIA FOR AUTOTHERMAL REACTORS;21
7.1.10;CONCLUSIONS AND DISCUSSION;23
7.1.11;Acknowledgement;23
7.1.12;REFERENCES;23
7.2;CHAPTER 2. DYNAMICS AND CONTROL OF DISTILLATION COLUMNS – A CRITICAL SURVEY;24
7.2.1;Abstract;24
7.2.2;1 Introduction;24
7.2.3;2 Dynamic modeling and simulation;25
7.2.4;3 Control;32
7.2.5;4 Identification;40
7.2.6;5 Complex dynamic behavior for simple columns with ideal thermodynamics;41
7.2.7;6 Dynamics and control for more complex cases;42
7.2.8;7 Needs for future work;43
7.2.9;Appendix 1. Derivation of constant molar flows assumption;43
7.2.10;References;43
7.3;CHAPTER 3. MODELING AND CONTROL OF POLYMERIZATION REACTORS;50
7.3.1;Abstract;50
7.3.2;Keywords;50
7.3.3;INTRODUCTION;50
7.3.4;POLYMERIZATION PROCESS MODELING;50
7.3.5;POLYMERIZATION PROCESS MONITORING;54
7.3.6;POLYMERIZATION PROCESS CONTROL;55
7.3.7;CONCLUDING REMARKS;56
7.3.8;REFERENCES;56
7.4;CHAPTER 4. MODELING AND CONTROL OF MICROELECTRONICS MATERIALS PROCESSING;60
7.4.1;Abstract;60
7.4.2;Keywords;60
7.4.3;INTRODUCTION;60
7.4.4;LPCVD REACTORS;62
7.4.5;PLASMA ETCH MODELING;64
7.4.6;PROCESS CONTROL OF DEPOSITION;65
7.4.7;PROCESS CONTROL OF PLASMA ETCHING;66
7.4.8;ACKNOWLEDGMENT;67
7.4.9;CONCLUSIONS;67
7.4.10;REFERENCES;67
8;PART II: CHEMICAL REACTORS MODELLING, ESTIMATION AND CONTROL;70
8.1;CHAPTER 5. COMPUTER-AIDED ANALYSIS OF IONIC REACTION SYSTEMS;70
8.1.1;Abstract;70
8.1.2;Keywords;70
8.1.3;INTRODUCTION;70
8.1.4;METHODOLOGY;71
8.1.5;THERMODYNAMIC ARGUMENTS;72
8.1.6;ESTIMATION OF PROPERTIES OF IONIC INTERMEDIATES;73
8.1.7;CONCLUSIONS;74
8.1.8;REFERENCES;75
8.2;CHAPTER 6.
APPROXIMATE DYNAMIC MODELS FOR CHEMICAL PROCESSES: A Comparative Study of Neural Networks and Nonlinear Time Series Modeling Techniques;76
8.2.1;Abstract;76
8.2.2;1.
INTRODUCTION;76
8.2.3;2. NEURAL NETWORKS WITH RADIAL BASIS FUNCTIONS;76
8.2.4;3. NONLINEAR TIME SERIES MODELS;77
8.2.5;4. MULTIVARIABLE CHEMICAL REACTOR SYSTEM;78
8.2.6;5. NONLINEAR INPUT-OUTPUT MODELS OF THE REACTOR SYSTEM;78
8.2.7;6. CONCLUSIONS;80
8.2.8;REFERENCES;81
8.3;CHAPTER 7.
STATE PREDICTION FOR CHEMICAL REACTORS USING FEEDBACK NEURAL NETWORKS;82
8.3.1;Abstract;82
8.3.2;Keywords;82
8.3.3;1 Introduction;82
8.3.4;2 Problem Description;82
8.3.5;3 Simulated Runs;83
8.3.6;4 The Feedback Structure;83
8.3.7;5 Compared Networks;84
8.3.8;6 Training Strategy;85
8.3.9;7 Results;85
8.3.10;8 Conclusions;87
8.3.11;References;87
8.4;CHAPTER 8.
CATALYTIC CRACKING MODELS DEVELOPED FOR PREDICTIVE CONTROL PURPOSES;88
8.4.1;Abstract;88
8.4.2;Keywords;88
8.4.3;INTRODUCTION;88
8.4.4;STATE-SPACE MODELING;88
8.4.5;FLUID CATALYTIC CRACKING;89
8.4.6;RESIDUAL CATALYTIC CRACKING;91
8.4.7;CONCLUSION;93
8.4.8;ACKNOWLEDGEMENT;93
8.4.9;REFERENCES;93
8.5;CHAPTER 9.
OBSERVABILITY AND CONTROLLABILITY OF CONTINUOUS INDUSTRIAL CRYSTALLISERS;94
8.5.1;Abstract;94
8.5.2;Keywords;94
8.5.3;Introduction;94
8.5.4;CSD Measurements;94
8.5.5;Process Dynamics;95
8.5.6;Experimental;96
8.5.7;Results;96
8.5.8;Conclusions;97
8.5.9;Acknowledgements;97
8.5.10;References;97
8.6;CHAPTER 10.
OBSERVER DESIGN AND SENSOR LOCATION IN DISTRIBUTED PARAMETER BIOREACTORS;100
8.6.1;Abstract;100
8.6.2;Keywords;100
8.6.3;INTRODUCTION;100
8.6.4;SYSTEM MODELLING;101
8.6.5;ON THE CONTROL GOAL;102
8.6.6;SENSOR LOCATION PROBLEM;102
8.6.7;OBSERVER DESIGN;102
8.6.8;SIMULATION STUDY;103
8.6.9;CONCLUDING REMARKS;103
8.6.10;REFERENCES;104
8.7;CHAPTER 11.
A DATA-BASED PROCESS MODELING APPROACH AND ITS APPLICATIONS;106
8.7.1;Abstract;106
8.7.2;1 Introduction;106
8.7.3;2 Data Re-sampling;107
8.7.4;3 Neural Net PLS Modeling;108
8.7.5;4 Variable Selection;109
8.7.6;5 Applications;110
8.7.7;6 Conclusion;110
8.7.8;References;111
8.8;CHAPTER 12.
DYNAMICS OF A PACKED BED REACTOR WITH REACTANT RECYCLE;112
8.8.1;Abstract;112
8.8.2;Keywords;112
8.8.3;1. INTRODUCTION;112
8.8.4;2. PROCESS;112
8.8.5;3. METHOD;113
8.8.6;4. RESULTS AND DISCUSSION;114
8.8.7;5. CONCLUSIONS;116
8.8.8;REFERENCES;116
8.8.9;APPENDIX;117
8.9;CHAPTER 13.
A DYNAMIC MODEL FOR THE METHANOL CONVERSION IN A FLUIDIZED BED REACTOR;118
8.9.1;Abstract;118
8.9.2;Keywords;118
8.9.3;INTRODUCTION;118
8.9.4;PILOT PLANT BRIEF DESCRIPTION;118
8.9.5;MTO KINETIC ANALYSIS AND FLUID-BED REACTOR MODEL;119
8.9.6;NUMERICAL EXAMPLE;120
8.9.7;DISCUSSION;121
8.9.8;REFERENCES;121
8.10;CHAPTER 14.
USE OF STOCHASTIC NEURAL NETWORKS FOR PROCESS CONTROL;124
8.10.1;ABSTRACT;124
8.10.2;KEYWORDS;124
8.10.3;1.0 INTRODUCTION.;124
8.10.4;2.0 NEURAL NETS AND PROCESS MODELLING;124
8.10.5;3.0 CONTROL ALGORITHMS;125
8.10.6;4.0 CONTINUOUS STIRRED TANK REACTOR;126
8.10.7;5.0 SIMULATION RESULTS;126
8.10.8;6.0 CONCLUSIONS;127
8.10.9;REFERENCES;127
8.11;CHAPTER 15.
MODELBASED CONTROL OF THE FERROSILICON PROCESS;130
8.11.1;Abstract;130
8.11.2;Keywords;130
8.11.3;INTRODUCTION;130
8.11.4;THE FERROSILICON PROCESS;130
8.11.5;MODEL OF THE SILICON PROCESS;133
8.11.6;A MODELBASED CONTROL STRATEGY;134
8.11.7;CONCLUSION;136
8.11.8;ACKNOWLEDGEMENTS;136
8.11.9;REFERENCES;136
8.12;CHAPTER 16. NONLINEAR ADAPTIVE CONTROL OF A CHEMICAL REACTOR USING SINGULAR PERTURBATION TECHNIQUES;138
8.12.1;ABSTRACT;138
8.12.2;KEYWORDS;138
8.12.3;1. INTRODUCTION;138
8.12.4;2. THE SINGULAR PERTURBATION METHODOLOGY;138
8.12.5;3. ADAPTIVE CONTROL OF NONLINEAR SYSTEMS;139
8.12.6;4. ADAPTIVE CONTROL OF NONLINEAR SINGULARLY PERTURBED SYSTEMS;140
8.12.7;5. APPLICATION TO THE CONTINUOUS STIRRED TANK REACTOR;141
8.12.8;6. ANALYSIS OF THE NUMERICAL RESULTS;142
8.12.9;7. CONCLUSIONS;142
8.12.10;REFERENCES;142
8.13;CHAPTER 17.
A SURVEY OF APPROXIMATE LINEARIZATION APPROACHES FOR CHEMICAL REACTOR CONTROL;144
8.13.1;Abstract;144
8.13.2;Key words;144
8.13.3;INTRODUCTION;144
8.13.4;APPROXIMATE LINEARIZATION TECHNIQUES;144
8.13.5;EXAMPLE # 1 - VAN DE VUSSE KINETICS;147
8.13.6;EXAMPLE # 2 - REVERSIBLE FIRST ORDER REACTIONS;148
8.13.7;DISCUSSION;148
8.13.8;References;149
8.14;CHAPTER 18.
ASYMPTOTICALLY EXACT LINEARIZATION OF CHEMICAL PROCESSES;150
8.14.1;Abstract;150
8.14.2;Keywords;150
8.14.3;INTRODUCTION;150
8.14.4;EXACT INPUT/OUTPUT LINEARIZATION;152
8.14.5;ASYMPTOTICALLY EXACT I/O LINEARIZATION;152
8.14.6;REFERENCES;155
8.15;CHAPTER 19.
ADAPTIVE LINEARIZING CONTROL OF NON-ISOTHERMAL REACTORS;156
8.15.1;Abstract;156
8.15.2;Keywords;156
8.15.3;1. INTRODUCTION;156
8.15.4;2. DYNAMICAL MODEL OF STIRRED TANK REACTORS;157
8.15.5;3. ADAPTIVE LINEARIZING CONTROL OF NON-ISOTHERMAL STIRRED TANK REACTORS;157
8.15.6;4. SIMULATION RESULTS;160
8.15.7;5. CONCLUSIONS;161
8.15.8;REFERENCES;161
8.16;CHAPTER 20.
CSTR PERFORMANCE LIMITATIONS DUE TO COOLING JACKET DYNAMICS;162
8.16.1;Abstract;162
8.16.2;Keywords;162
8.16.3;INTRODUCTION;162
8.16.4;DYNAMIC MODELS;163
8.16.5;STABILITY ANALYSIS;164
8.16.6;CASCADE CONTROL SYSTEM DESIGN;165
8.16.7;SUMMARY;166
8.16.8;Acknowledgement;166
8.16.9;REFERENCES;166
8.16.10;APPENDIX;167
8.17;CHAPTER 21.
RELIABLE CONTROL OF CHEMICAL PROCESSES WITH A SUPERVISORY KNOWLEDGE-BASED SYSTEM;168
8.17.1;Abstract;168
8.17.2;INTRODUCTION;168
8.17.3;PROCESS, CONTROL AND KNOWLEDGE-BASED SYSTEM DESCRIPTIONS;168
8.17.4;IMC STRUCTURE A N D IMPLEMENTATION;169
8.17.5;CONTROLLER RECONFIGURATION;171
8.17.6;CONCLUSIONS;172
8.17.7;REFERENCES;172
8.18;CHAPTER 22.
ON-LINE ESTIMATION AND CONTROL OF POLYMERIZATION REACTORS;174
8.18.1;Abstract;174
8.18.2;Keywords;174
8.18.3;INTRODUCTION;174
8.18.4;PROCESS DESCRIPTION;174
8.18.5;REACTOR TEMPERATURE CONTROL PROBLEM;175
8.18.6;ESTIMATION OF HEAT TRANSFER COEFFICIENT AND MOLECULAR WEIGHT;176
8.18.7;RESULTS AND DISCUSSION;177
8.18.8;ACKNOWLEDGEMENT;179
8.18.9;REFERENCES;179
8.19;CHAPTER 23.
MODELING AND CONTROL OF CONTINUOUS MELT POLYCONDENSATION REACTORS FOR THE SYNTHESIS OF THERMOPLASTIC POLYMERS;180
8.19.1;Abstract;180
8.19.2;Keywords;180
8.19.3;INTRODUCTION;180
8.19.4;PROCESS MODELING;180
8.19.5;PREPOLYMERIZATION PROCESS;182
8.19.6;FINISHING POLYMERIZATION PROCESS;183
8.19.7;CONCLUSIONS;184
8.19.8;ACKNOWLEDGMENT;184
8.19.9;REFERENCES;184
8.20;CHAPTER 24.
NONLINEAR MULTIVARIABLE CONTROL OF PRODUCT PROPERTIES IN AN INDUSTRIAL GAS PHASE POLYETHYLENE REACTOR;186
8.20.1;Abstract;186
8.20.2;Keywords;186
8.20.3;INTRODUCTION;186
8.20.4;OPTIMAL GRADE TRANSITION TRAJECTORIES;187
8.20.5;ON-LINE PRODUCT PROPERTY CONTROL;187
8.20.6;SUMMARY;189
8.20.7;ACKNOWLEDGMENTS;189
8.20.8;REFERENCES;189
8.21;CHAPTER 25.
A COMPARISON OF STRATEGIES FOR THE CONTROL OF A POLYPROPENE REACTOR;192
8.21.1;Abstract;192
8.21.2;Keywords;192
8.21.3;1 Introduction;192
8.21.4;2 Control strategies;192
8.21.5;3 Evaluation of the strategies;193
8.21.6;4 Discussion;195
8.21.7;Acknowledgment;196
8.21.8;5 References;196
8.21.9;Appendix A The polypropene reactor system;196
9;PART III: DISTILLATION COLUMNS MODELLING, ESTIMATION AND CONTROL;198
9.1;CHAPTER 26.
AN ANALYTICAL APPROACH TO MODELLING IN DISTILLATION CONTROL;198
9.1.1;Abstract;198
9.1.2;Keywords;198
9.1.3;INTRODUCTION;198
9.1.4;MODELLING APPROACH;198
9.1.5;MODEL ANALYSIS;200
9.1.6;EXPERIMENTAL STUDY;202
9.1.7;CONCLUSIONS;205
9.1.8;REFERENCES;205
9.2;CHAPTER 27.
THE UNIQUE CHALLENGES OF CRYOGENIC DISTILLATION COLUMN CONTROL FOR INTEGRATED COAL GASIFICATION COMBINED CYCLE APPLICATIONS;206
9.2.1;ABSTRACT;206
9.2.2;KEYWORDS;206
9.2.3;INTRODUCTION;206
9.2.4;METHODOLOGY;208
9.2.5;ANALYSIS OF POSSIBLE CONTROL SCHEMES;209
9.2.6;RESULTS AND CONCLUSION;210
9.2.7;Acknowledgements;210
9.2.8;REFERENCES;212
9.3;CHAPTER 28.
DYNAMICS AND CONTROL OF UNSTABLE DISTILLATION COLUMNS;214
9.3.1;Abstract;214
9.3.2;1 Introduction;214
9.3.3;2 Results on Steady-State Multiplicity in Ideal Distillation;214
9.3.4;3 Open-Loop Dynamics and Instability for LwV-configuration;215
9.3.5;4 Effect of Multiplicity and Instability on Column Operation;216
9.3.6;5 Other Bifurcation Parameters;217
9.3.7;6 Instability with the DwV-Configuration;217
9.3.8;7 Conclusions;219
9.3.9;NOMENCLATURE;219
9.3.10;References;219
9.4;CHAPTER 29.
RELATING THE DRD STRUCTURE TO CONVENTIONAL MODEL BASED CONTROLLERS;220
9.4.1;Abstract;220
9.4.2;Keywords;220
9.4.3;INTRODUCTION;220
9.4.4;CONTROL STRUCTURES IN DISTILLATION CONTROL;220
9.4.5;PARAMETRIZATION OF THE DRD STRUCTURE;221
9.4.6;STABILITY AND ROBUSTNESS OF THE STATIC DRD STRUCTURE;222
9.4.7;DYNAMIC EXTENSION OF THE DRD STRUCTURE;223
9.4.8;SENSITIVITY;224
9.4.9;CONCLUSIONS;224
9.4.10;ACKNOWLEDGMENTS;224
9.4.11;NOTATION;224
9.4.12;REFERENCES;224
9.5;CHAPTER 30.
CONTROL STRUCTURES FOR A SIDESTREAM DISTILLATION COLUMN SEPARATING A TERNARY MIXTURE;226
9.5.1;Abstract;226
9.5.2;Keywords;226
9.5.3;INTRODUCTION;226
9.5.4;PLANT DESCRIPTION;226
9.5.5;ACTUATOR CONFIGURATION;228
9.5.6;CONCLUSIONS;231
9.5.7;REFERENCES;231
9.6;CHAPTER 31.
SELECTION OF THE BEST CONTROL CONFIGURATION FOR AN INDUSTRIAL DISTILLATION COLUMN;232
9.6.1;Abstract;232
9.6.2;Keywords;232
9.6.3;INTRODUCTION;232
9.6.4;SYSTEM STUDIED;233
9.6.5;EXPERIMENTAL PROCEDURE;233
9.6.6;STEADY-STATE ANALYSIS;233
9.6.7;EIGENSTRUCTURE STUDIES;235
9.6.8;DYNAMIC ANALYSIS;235
9.6.9;CONCLUSION;237
9.6.10;REFERENCES;237
9.7;CHAPTER 32.
NON-LINEAR ANALYSIS OF DISTILLATION CONTROL STRUCTURES;238
9.7.1;Abstract;238
9.7.2;Keywords;238
9.7.3;1. INTRODUCTION;238
9.7.4;2. INPUT-OUTPUT NONLINEAR DYNAMIC LEFT-INVERSION;238
9.7.5;3. THE MODEL OF THE COLUMN;239
9.7.6;4. STATEMENT OF THE PROBLEM;240
9.7.7;5. ANALYSIS OF DISTILLATION CONTROL CONFIGURATIONS;240
9.7.8;6. CONCLUSIONS;242
9.7.9;7. BIBLIOGRAPHY;242
9.8;CHAPTER 33.
A COMPARATIVE STUDY OF LINEAR AND NONLINEAR MULTIVARIABLE BINARY DISTILLATION COLUMN CONTROL;244
9.8.1;Abstract;244
9.8.2;Keywords;244
9.8.3;THE PROCESS;244
9.8.4;THE BILINEAR MODEL AND LINE ARIZ ....;244
9.8.5;MODEL PREDICTIVE CONTROLLER;245
9.8.6;PI CONTROLLER;246
9.8.7;COMPARISON OF CONTROLLERS;247
9.8.8;CONCLUSION;247
9.8.9;REFERENCES;247
9.9;CHAPTER 34.
FEEDFORWARD/FEEDBACK CONTROL OF A BINARY HIGH PURITY DISTILLATION COLUMN;250
9.9.1;Abstract;250
9.9.2;Keywords;250
9.9.3;Objective;250
9.9.4;Conclusion;255
9.9.5;References;255
9.10;CHAPTER 35.
PREDICTIVE CONTROL OF DISTILLATION COLUMNS USING DYNAMIC NEURAL NETWORKS;256
9.10.1;Abstract;256
9.10.2;Keywords;256
9.10.3;INTRODUCTION;256
9.10.4;ARTIFICIAL NEURAL NETWORK MODELS;257
9.10.5;NONLINEAR PREDICTIVE CONTROL USING NEURAL NETWORKS;257
9.10.6;APPLICATION TO A BINARY DISTILLATION COLUMN;259
9.10.7;CONCLUDING REMARKS;260
9.10.8;REFERENCES;260
9.11;CHAPTER 36.
APPLICATION OF A BILINEAR LONG-RANGE PREDICTIVE CONTROL METHOD IN A DISTILLATION PROCESS;262
9.11.1;Abstract;262
9.11.2;Keywords;262
9.11.3;INTRODUCTION;262
9.11.4;THE BILINEAR CARIMA MODEL AND OUTPUT PREDICTION;262
9.11.5;THE LONG-RANGE PREDICTIVE CONTROL LAWS;263
9.11.6;BASIC PROPERTIES AND ROBUSTNESS;264
9.11.7;APPLICATION STUDY;265
9.11.8;CONCLUSIONS;267
9.11.9;REFERENCES;267
9.12;CHAPTER 37.
COMBINING ADAPTIVE AND NEURAL CONTROL FOR DISTILLATION CONTROL;268
9.12.1;Abstract;268
9.12.2;Keywords;268
9.12.3;INTRODUCTION;268
9.12.4;INTELLIGENCE;268
9.12.5;ARTIFICIAL INTELLIGENCE;268
9.12.6;NEURAL INTELLIGENCE;269
9.12.7;CONTROL;270
9.12.8;ADAPTIVE CONTROL;270
9.12.9;NEURAL NETWORKS;270
9.12.10;COMBINING;271
9.12.11;APPLICATION FOR DISTILLATION;271
9.12.12;CONCLUSION;272
9.12.13;ACKNOWLEDGEMENT;272
9.12.14;REFERENCES;272
10;PART IV: BATCH PROCESSES MODELLING AND CONTROL;274
10.1;CHAPTER 38.
DISCRETE-EVENT CONTROLLED SYSTEMS IN THE CHEMICAL PROCESSING INDUSTRY;274
10.1.1;ABSTRACT;274
10.1.2;KEYWORDS;274
10.1.3;DED SYSTEMS-THE NEW WAVE?;274
10.1.4;WHAT ARE DEDS?;274
10.1.5;BASIC STRUCTURE OF DEDSs;275
10.1.6;DEDSs;276
10.1.7;THEORETICAL DEVELOPMENTS;276
10.1.8;APPLICATIONS;277
10.1.9;OPEN PROBLEMS, ANTICIPATED FUTURE DIRECTIONS;277
10.1.10;LITERATURE;278
10.2;CHAPTER 39.
THE DYNAMIC MODELING AND OPTIMIZATION OF AN INDUSTRIAL BATCH REACTOR;280
10.2.1;Abstract;280
10.2.2;Keywords;280
10.2.3;Introduction;280
10.2.4;Background;280
10.2.5;Kinetics;280
10.2.6;Reactor Model;281
10.2.7;Optimal Temperature Profile;282
10.2.8;Conclusions;283
10.2.9;Acknowledgements;284
10.2.10;References;284
10.3;CHAPTER 40.
THE DESIGN AND SYNTHESIS OF BATCH/SEMICONTINUOUS PROCESSES;286
10.3.1;Abstract;286
10.3.2;keywords;286
10.3.3;Introduction;286
10.3.4;Sizing problem;286
10.3.5;Retrofitting;288
10.3.6;Synthesis problem;288
10.3.7;Intermediate storage tank;290
10.3.8;Conclusion;291
10.3.9;REFERENCES;291
10.4;CHAPTER 41.
MONITORING DISCONTINUOUS REACTORS USING FACTOR-ANALYTICAL TECHNIQUES;292
10.4.1;Abstract;292
10.4.2;Key words;292
10.4.3;1 Introduction;292
10.4.4;2 Problems formulation;292
10.4.5;3 Incremental TFA;294
10.4.6;4 Simulated example;295
10.4.7;5 Conclusions;297
10.4.8;6 References;297
10.5;CHAPTER 42.
DISSOLVED OXYGEN CONTROL USING AN AUTOMATIC TUNING PID CONTROLLER;298
10.5.1;Abstract;298
10.5.2;Keywords;298
10.5.3;INTRODUCTION;298
10.5.4;AUTOMATIC TUNER THEORY;299
10.5.5;BAKERS' YEAST MODEL;299
10.5.6;ON-LINE RESULTS;300
10.5.7;CONCLUSIONS;301
10.5.8;ACKNOWLEDGEMENTS;301
10.5.9;NOMENCLATURE;301
10.5.10;REFERENCES;301
10.6;CHAPTER 43.
OPTIMIZATION AND CONTROL OF AN INDUSTRIAL SCALE MULTIVARIABLE NONLINEAR MICROALGAE FERMENTATION;304
10.6.1;Abstract;304
10.6.2;Keywords;304
10.6.3;INTRODUCTION;304
10.6.4;EXPERIMENTAL MATERIALS AND METHODS;305
10.6.5;THE DYNAMICAL MODEL;305
10.6.6;OPTIMIZATION METHODS;305
10.6.7;CONTROL;307
10.6.8;MODEL PARAMETER SENSITIVITY;309
10.6.9;CONCLUSIONS;309
10.6.10;REFERENCES;309
10.7;CHAPTER 44.
BATCH TIME OPTIMIZATION USING BOTH REACTANT DOSING AND EXTERNAL COOLING FOR TIGHT TEMPERATURE CONTROL;310
10.7.1;Abstract;310
10.7.2;Keywords;310
10.7.3;INTRODUCTION;310
10.7.4;PROBLEM DEFINITION;311
10.7.5;HEAT BALANCE;312
10.7.6;NEW CONTROL STRATEGY;312
10.7.7;SIMULATION;313
10.7.8;CONCLUSIONS AND FURTHER RESEARCH;313
10.7.9;REFERENCES;313
10.8;CHAPTER 45.
APPLICATION OF NONLINEAR MODEL PREDICTIVE CONTROL TO OPTIMAL BATCH DISTILLATION;316
10.8.1;Abstract;316
10.8.2;Keywords;316
10.8.3;INTRODUCTION;316
10.8.4;BATCH DISTILLATION AND NMPC;316
10.8.5;MODELING;317
10.8.6;CONTROL;318
10.8.7;OPTIMIZATION AND SIMULATION RESULTS;319
10.8.8;CONCLUSIONS;321
10.8.9;ACKNOWLEDGEMENTS;321
10.8.10;References;321
10.9;CHAPTER 46.
OPERATION STRATEGIES FOR REVERSE-OSMOSIS MEMBRANE FOULING IN THE DAIRY INDUSTRY;322
10.9.1;Abstract;322
10.9.2;Keywords;322
10.9.3;INTRODUCTION;322
10.9.4;MEMBRANE FILTRATION IN THE FOOD INDUSTRY;323
10.9.5;PROCESS MODEL;323
10.9.6;PROCESS DESCRIPTION;323
10.9.7;RESULTS OF DYNAMIC OPTIMIZATION;324
10.9.8;OBJECTIVE FUNCTION AND PRODUCTION CONSTRAINTS;324
10.9.9;ROBUSTNESS OF OPTIMAL OPERATION STRATEGIES;325
10.9.10;ECONOMIC EVALUATION;325
10.9.11;CONCLUSIONS;325
10.9.12;ACKNOWLEDGEMENT;326
10.9.13;LIST OF SYMBOLS;326
10.9.14;REFERENCES;326
10.10;CHAPTER 47.
INTELLIGENT OPERATION SUPPORT SYSTEM FOR A BATCH SULPHITE PULPING DIGESTER;328
10.10.1;Abstract;328
10.10.2;Keywords;328
10.10.3;INTRODUCTION;328
10.10.4;PROBLEM DEFINITION;328
10.10.5;PROBLEM SOLVING STRATEGY;329
10.10.6;KNOWLEDGE ACQUISITION;329
10.10.7;KNOWLEDGE REPRESENTATION;329
10.10.8;SYSTEM CONFIGURATION;330
10.10.9;OTHER ADVANCED SYSTEM FEATURES;331
10.10.10;EVALUATION;331
10.10.11;CONCLUSIONS;332
10.10.12;ACKNOWLEDGEMENTS;332
10.10.13;REFERENCES;332
11;PART V: GENERAL SYSTEMS. MODELLING, ESTIMATION AND CONTROL;334
11.1;CHAPTER 48.
HYBRID NEURAL NETWORK/ALGORITHMIC APPROACHES TO SYSTEM IDENTIFICATION;334
11.1.1;Abstract;334
11.1.2;Keywords;334
11.1.3;INTRODUCTION;334
11.1.4;ARTIFICIAL NEURAL NETWORKS;335
11.1.5;NEURAL NETWORKS AND NONLINEAR IDENTIFICATION FOR SYSTEM IDENTIFICATION;336
11.1.6;A HYBRID SYSTEM IDENTIFIER;337
11.1.7;EXTENSIONS;338
11.1.8;A CONCLUDING REMARK;339
11.1.9;REFERENCES;339
11.2;CHAPTER 49.
INTEGRATING NEURAL NETWORKS WITH FIRST PRINCIPLES MODELS FOR DYNAMIC MODELING;340
11.2.1;Abstract;340
11.2.2;1 Introduction;340
11.2.3;2 Integrated Neural Network (INN) Models;341
11.2.4;3 Case Study: A Complex Chemical Reactor System;342
11.2.5;4 Results;344
11.2.6;5 Conclusions;345
11.2.7;6 References;345
11.3;CHAPTER 50.
TIME DELAY COMPENSATION FOR NONLINEAR PROCESSES;346
11.3.1;Abstract;346
11.3.2;Keywords;346
11.3.3;1. INTRODUCTION;346
11.3.4;2. TIME DELAY COMPENSATION TECHNIQUES;346
11.3.5;3. A NEW TIME DELAY COMPENSATION STRATEGY;348
11.3.6;4 . SIMULATION STUDY: CONTINUOUS STIRRED TANK REACTOR;349
11.3.7;5. CONCLUSIONS;350
11.3.8;REFERENCES;350
11.4;CHAPTER 51.
RECURSIVE PROCESS IDENTIFICATION UNDER VARIABLE FLOW AND VOLUME;352
11.4.1;Abstract;352
11.4.2;Keywords;352
11.4.3;1 INTRODUCTION;352
11.4.4;2 BACKGROUND;352
11.4.5;3 PROCESSES WITH VARIABLE FLOW AND CON STANTVOLUME;353
11.4.6;4 PROCESSES WITH VARIABLE VOLUME AND FLOW - ARBITRARY FLOW PATTERN;354
11.4.7;5 PERFECT MIXERS WITH VARIABLE VOLUME AND FLOW;355
11.4.8;6 TRACKING OF FLOW PATTERN DATA;356
11.4.9;7 DISCUSSION;357
11.4.10;REFERENCES;357
11.5;CHAPTER 52.
ON THE STABILITY OF NONLINEAR QUADRATIC DYNAMIC MATRIX CONTROL;358
11.5.1;Abstract;358
11.5.2;Keywords;358
11.5.3;1 Introduction;358
11.5.4;2 State Estimation NLQDMC;358
11.5.5;3 Illustration;360
11.5.6;4 Stability Analysis;361
11.5.7;5 Conclusions;363
11.5.8;Acknowledgements;363
11.5.9;References;363
11.6;CHAPTER 53.
A NONLINEAR PREDICTIVE CONTROL STRATEGY BASED ON RADIAL BASIS FUNCTION NETWORKS;364
11.6.1;Abstract;364
11.6.2;Keywords;364
11.6.3;INTRODUCTION;364
11.6.4;RADIAL BASIS FUNCTION MODELS OF DYNAMICAL SYSTEMS;364
11.6.5;AN IDENTIFICATION METHOD BASED ON RECIPROCAL MULTIQUADRIC FUNCTIONS;365
11.6.6;A NONLINEAR PREDICTIVE CONTROL STRATEGY BASED ON RADIAL BASIS FUNCTION NETWORKS;366
11.6.7;AN RBF NETWORK NONLINEAR PREDICTIVE CONTROLLER;366
11.6.8;SIMULATION STUDIES;367
11.6.9;CONCLUSIONS;368
11.6.10;REFERENCES;368
11.7;CHAPTER 54.
IDENTIFICATION AND ROBUST CONTROL OF pH IN A CSTR USING STEP RESPONSE DATA;370
11.7.1;Abstract;370
11.7.2;Keywords;370
11.7.3;INTRODUCTION;370
11.7.4;EXPERIMENTAL SYSTEM -THEORETICAL MODEL;371
11.7.5;LAGUERRE NETWORK -ROBUST CONTROLLER DESIGN;371
11.7.6;QUANTIFICATION OF MODELLING ERROR - CLOSED-LOOP RESULTS;372
11.7.7;CONCLUSIONS;374
11.7.8;REFERENCES;374
11.8;CHAPTER 55.
ELECTROVACUUM GLASS QUALITY CONTROL SYSTEM TRANSFERRING;376
11.8.1;Abstract;376
11.8.2;Keywords;376
11.8.3;1. INTRODUCTION;376
11.8.4;2.FAULT DETECTION PROBLEM FORMULATION;377
11.8.5;3.DECISION SUPPORT FAULT DETECTION PROCEDURE;377
11.8.6;4. IMPLEMENTATION;378
11.8.7;REFERENCES;378
12;AUTHOR INDEX;380
13;KEYWORD INDEX;382